How to compare the training performance of a model on different data input?
Well, this is more of a subjective question but I will give my best shot. 

Regarding your 1st question, the nature of methods in deep learning states that you should experiment otherwise you will only have weak intuitions. So which dataset should you choose? I would say, select your primary concerns since you cannot try everything. If it's time, make a little random or grid search over hyperparameters and observe speed of convergence in the datasets then choose the best one. If it's accuracy, ideally you should make an input analysis on distribution of data, etc. If you can analyze it well, you are expected to obtain best result from the chosen dataset. So training 30 epochs each dataset and choosing the one with lowest loss is not a safe approach. Maybe your model will converge at 40th epoch for the worst dataset in the 30th epoch but it will be much more robust. My final advice is to set a threshold for your evaluation metrics, once you reach them in any of your dataset choose that one -assuming datasets are more or less equal in number of instances. In this way you are at least know that the dataset you choose satisfies your expectations.

For your 2nd question, seeding during comparisons is a good approach though in the long run it won't matter much. Hence there is no harm in seeding unless you are very very very unlucky.